YashasSamaga

import cv2
import time

CONFIDENCE_THRESHOLD = 0.2
NMS_THRESHOLD = 0.4
COLORS = [(0, 255, 255), (255, 255, 0), (0, 255, 0), (255, 0, 0)]

class_names = []
with open("classes.txt", "r") as f:
    class_names = [cname.strip() for cname in f.readlines()]

amarion35

from keras import backend as K
from keras.engine.base_layer import Layer, InputSpec
from keras import activations
from keras.layers.convolutional import _Conv
import numpy as np

class TDNN(_Conv):
    # Original TDNN
    # A. Waibel, T. Hanazawa, G. Hinton, K. Shikano and K. J. Lang,
    # "Phoneme recognition using time-delay neural networks,"

nicolov

*.pyc
*.tar.gz
*.mp4

joelouismarino

from __future__ import print_function
import imageio
from PIL import Image
import numpy as np
import keras

from keras.layers import Input, Dense, Conv2D, MaxPooling2D, AveragePooling2D, ZeroPadding2D, Dropout, Flatten, Concatenate, Reshape, Activation
from keras.models import Model
from keras.regularizers import l2
from keras.optimizers import SGD

alghanmi

#include <iostream>
#include <string>
#include <curl/curl.h>


static size_t WriteCallback(void *contents, size_t size, size_t nmemb, void *userp)
{
    ((std::string*)userp)->append((char*)contents, size * nmemb);
    return size * nmemb;
}